NVIDIA NCP-ADS

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. In Python, when working with large datasets using pandas, which of the following methods are best for improving performance and efficiency when applying operations on DataFrames? (Select two)

A) Using apply() function over DataFrame rows
B) Using iterrows() for iterating through DataFrame rows
C) Using for loops to apply operations row by row
D) Using vectorized operations (e.g., element-wise arithmetic)
E) Using map() function to apply a function element-wise

2. You are analyzing a time-series dataset that represents temperature readings from an industrial sensor. Your goal is to detect anomalies that may indicate sensor failures or environmental changes.
Which of the following methods would be the most appropriate statistical technique for detecting anomalies in this time-series dataset?

A) Moving Average Smoothing
B) One-Hot Encoding
C) Principal Component Analysis (PCA)
D) Z-score (Standard Score) Analysis

3. You have trained a machine learning model and are deploying it into production in a cloud-based environment. You need to ensure the model is continuously monitored for performance, including drift in input data and prediction quality.
Which of the following techniques should you use to effectively monitor the model in production? (Select two)

A) Set up continuous model retraining pipelines triggered by data drift or performance degradation.
B) Restrict access to the model's API to minimize the number of calls and reduce monitoring overhead.
C) Implement model versioning and rollback procedures to maintain consistency in production models.
D) Use manual inspection of model predictions periodically to assess accuracy in production.

4. A data scientist is using NVIDIA RAPIDS to perform statistical analysis as part of exploratory data analysis (EDA) on a dataset containing millions of product reviews. They need to compute basic descriptive statistics such as mean, median, and variance efficiently.
Which of the following methods is the most appropriate for performing these calculations on GPUs?

A) Use cuDF's built-in statistical functions like .mean(), .median(), and .var()
B) Convert the dataset into a PyTorch tensor and use PyTorch's statistical methods
C) Use NumPy's statistical functions, such as numpy.mean() and numpy.var()
D) Use a traditional SQL database to compute statistics and then transfer results to the GPU

5. You are working with structured tabular data in a cloud-based GPU environment.
Your dataset contains the following columns:
Column Name Example Values Data Type Needed
user_id 15432, 98765, 43210 Integer
purchase_amt 12.99, 35.50, 100.75 Floating Point
category 'Books', 'Electronics' Categorical
Which of the following is the most optimal approach to assign data types to these columns to ensure efficient memory usage and computational performance?

A) 1. df['user_id'] = df['user_id'].astype('float32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
B) 1. df['user_id'] = df['user_id'].astype('int16')
2. df['purchase_amt'] = df['purchase_amt'].astype('float16')
3. df['category'] = df['category'].astype('string')
C) 1. df['user_id'] = df['user_id'].astype('int32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float32')
3. df['category'] = df['category'].astype('category')
D) 1. df['user_id'] = df['user_id'].astype('int64')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')

Solutions: